Types Of Winding Faults Research Articles

Generalized regression neural network application for fault type detection in distribution transformer windings considering statistical indices

PurposeThis study aims to use frequency response analysis, a powerful tool to detect the location and types of transformer winding faults. Proposing an effective intelligent approach for interpreting the frequency responses is the most crucial problem of this method and has created many challenges.Design/methodology/approachHeat maps based on appropriate statistical indices have been supplied to depict the variations in the frequency responses associated with each fault type, fault location and fault extent along the windings. Also, after analyzing the results of artificial neural network (ANN) techniques, the generalized regression neural network method is introduced as the most effective solution for the classification of transformer winding faults.FindingsUsing a comparative approach, the performance of the used indices and ANN techniques are evaluated. The results showed the proper performance of Lin’s concordance coefficient (LCC) index and the amplitude (Amp) part of the frequency response. The proposed fitting percentage (FP) index can assist the intelligent classifiers in diagnosing the radial deformation (RD) fault with the highest accuracy considering all frequency response components in the classification procedure of winding faults.Practical implicationsVarious ANN techniques are used to detect and determine the type of four important faults of transformer winding, i.e. axial displacement, RD, disc space variation and short circuit. Various statistical indices, such as cross-correlation factor, LCC, standard difference area, sum of errors, normalized root-mean-square deviation and FP, are used to extract the features of the frequency responses to consider as the ANN inputs. In addition, different components of the frequency response, such as Amp, argument, real and imaginary parts are examined in this paper. To implement the proposed procedure, step by step, various types of winding faults with different locations and extents are applied on the 20 kV winding of a 1.6 MVA distribution transformer.Originality/valueContributions have been made in identifying and diagnosing transformer winding defects through the use of appropriate algorithms for future research.

Diagnostic of transformer winding deformation fault types using continuous wavelet transform of pulse response

Impulse Frequency Response Analysis (IFRA) provides an alternative technique for detection of mechanical type faults in power transformers among researchers. However, the conventional frequency response curve obtained from the time domain transient response only contains the frequency domain information of signal, and the time domain information is neglected. Furthermore, the traditional approach of using the fast Fourier transform (FFT) in processing the non-stationary signal could result in lack of useful information. This paper proposes a novel winding deformation fault diagnostic technique which is modified from IFRA, the detected transient signals of pulse response are processed based on the continuous wavelet transform (CWT) algorithm, and the wavelet time-frequency diagram are plotted and analyzed. The simulation of a simple transformer high frequency model and experiment analysis show that the various types of winding deformation can be distinguished by the proposed technique. The data processing result by the wavelet transform conforms with the variation pattern of frequency response to different winding fault types. Besides, the verification result of experiment indicates that the wavelet time-frequency diagram obtained by the proposed technique shows better performance than the frequency response curves obtained by the conventional FFT on processing the transient signals.

Diagnosis and clustering of power transformer winding fault types by cross‐correlation and clustering analysis of FRA results

The power transformer is one of the vital and substantial elements of each country's power grid which not only require high investment, but they are also important in terms of economy, social, political, and strategy. Since this equipment is exposed to different electrical and mechanical winding faults during operation, they should be monitored continuously. One of the main monitoring methods is the use of frequency response analysis (FRA), which has a high sensitivity. The main challenge of the FRA is that the detecting task of the status of the transformer is done by a specialist and with a visual evaluation of the records. To overcome this problem, first, frequency responses in the healthy and present states are calculated through simulation of electrical and mechanical fault in the winding of the transformer and then, new statistical methods are used to interpret FRA results based on the obtained transfer function. In this study, for the first time, clustering analysis and cross-correlation methods are used to interpret FRA results for clustering and diagnosis of different short circuits turns, axial displacement, and radial deformation. Results and simulations verify ability and advantage of these methods in detection and determination of different faults.

Identification of Power Transformer Winding Fault Types by a Hierarchical Dimension Reduction Classifier

Frequency response analysis (FRA) demonstrates significant advantages in the diagnosis of transformer winding faults. The instrument market desires intelligent diagnostic functions to ensure that the FRA technique is more practically useful. In this paper, a hierarchical dimension reduction (HDR) classifier is proposed to identify types of typical incipient winding faults. The classifier procedure is hierarchical. First, measured frequency response (FR) curves are preprocessed using binarization and binary erosion to normalize FR data. Second, the pre-processed data are divided into groups according to the definition of dynamic frequency sub-bands. Then, hybrid algorithms comprised of two conventional and two novel quantitative indices are used to reduce the dimension of the FR data and extract the features for identifying typical types of transformer winding faults. The classifier provides an integration of a priori expertise and quantitative analysis in the furtherance of the automatic identification of FR data. Twenty-six sets of FR data from different types of power transformers with multiple types of winding faults were collected from an experimental simulation, literature, and real tests performed by a grid company. Finally, real case studies were conducted to verify the performance of the HDR classifier in the automatic identification of transformer winding faults.

Characterization of coil faults in an axial flux variable reluctance PM motor

In recent years, variable reluctance (VR) and switch reluctance (SR) motors have been proposed for use in applications requiring a degree of fault tolerance. A range of topologies of brushless SR and VR permanent magnet (PM) motors are not susceptible to some types of faults, such as phase-to-phase shorts, and can often continue to function in the presence of other faults. In particular, coil winding faults in a single stator coil may have relatively little effect on motor performance, but may affect overall motor reliability, availability, and longevity. It is important to distinguish between, and characterize, various winding faults for maintenance and diagnostic purposes. These fault characterization and analysis results are a necessary first step in the process of motor fault detection and diagnosis for this motor topology. This paper examines rotor velocity damping due to stator winding turn-to-turn short faults in a fault-tolerant axial flux variable reluctance PM motor. In this type of motor, turn-to-turn shorts due to insulation failures have similar I-V characteristics to coil faults resulting from other problems, such as faulty maintenance or damage due to impact. In order to investigate the effects of such coil faults, a prototype axial flux variable reluctance PM motor was constructed. The motor was equipped with experimental fault simulation stator windings capable of simulating these and other types of stator winding faults. This paper will focus on two common types of winding faults and their effects on rotor velocity in this type of motor.

Characterization of Coil Faults in an Axial Flux Variable Reluctance PM Motor

In recent years, variable reluctance (VR) and switch reluctance (SR) motors have been proposed for use in applications requiring a degree of fault tolerance. A range of topologies of brushless SR and VR permanent magnet (PM) motors are not susceptible to some types of faults, such as phase-to-phase shorts, and can often continue to function in the presence of other faults. In particular, coil winding faults in a single stator coil may have relatively little effect on motor performance, but may affect overall motor reliability, availability, and longevity. It is important to distinguish between, and characterize, various winding faults for maintenance and diagnostic purposes. These fault characterization and analysis results are a necessary first step in the process of motor fault detection and diagnosis for this motor topology. This paper examines rotor velocity damping due to stator winding turn-to-turn short faults in a fault-tolerant axial flux variable reluctance PM motor. In this type of motor, turn-to-turn shorts due to insulation failures have similar I-V characteristics to coil faults resulting from other problems, such as faulty maintenance or damage due to impact. In order to investigate the effects of such coil faults, a prototype axial flux variable reluctance PM motor was constructed. The motor was equipped with experimental fault simulation stator windings capable of simulating these and other types of stator winding faults. This paper will focus on two common types of winding faults and their effects on rotor velocity in this type of motor.